A Faster and Cheaper Strategy for Drug Discovery

It costs an average of $350 billion to bring a drug to market. For every drug made available to the consumer, nine are abandoned during development. Given these costs, most pharmaceutical companies are limited to the earliest stage: discovery.

Drug discovery is a multi-billion dollar business. Forbes has estimated that it costs $350 billion to bring one drug to market (1). Frequent inability to prove safety and efficacy in clinical trials means that for every drug that is brought to market, nine are abandoned during development (2). To increase the probability of success each company may evaluate multiple candidates at once, pushing the cost to over $5 billion per successful pharmaceutical (1). Given these high stakes, most organizations with an interest in pharmaceutical development are limited to the earliest stage: discovery.

All sectors of the pharmaceutical industry are invested in finding faster and cheaper ways to discover the next successful drug. There have been few major improvements in clinical development, where candidates progress from preclinical testing through FDA approval. Instead, most improvements are made by streamlining the discovery of promising candidates from large libraries of small-molecule compounds with as-yet undetermined functions.

The competitive landscape for pharmaceutical development is like a boxing ring. The floor represents the landscape of human disease, every condition that we would like to treat or cure. The three ropes represent the chemical, biological, and physical limits that determine how we design and build pharmaceuticals.

The competitive landscape for pharmaceutical development can be compared to a boxing ring.

The floor represents the landscape of human disease, every condition that we would like to treat or cure.

The three ropes represent the chemical, biological, and physical limits that determine how we design and build pharmaceuticals.

In chemical terms, the golden rule that constrains pharmaceutical development is Lipinski’s Rule of Five. The rule was formulated by Christopher Lipinski in 1997 to describe the most common chemical characteristics of a successful drug (3). In terms of biology, pharmaceutical development is bounded by pharmacokinetics. Michael Hann, Director of Structural Biology for Europe at GlaxoSmithKline, describes a successful pharmacokinetic profile as the intersection of 6 characteristics: potency, safety, stability, specificity, absorption, and solubility (4). That is, optimally, a good drug can be taken orally in small amounts to target the specific cause of a disease. It is a long and winding road from pharmaceutical candidate (X1) to marketable drug (Xn), so organizations exert considerable effort in identifying the best candidates.

Before a candidate can enter the boxing ring, it must take part in a number of smaller matches aimed at screening out the poor performers and advancing robust champions. These early tournaments are strategically organized so that a collection of strong candidates is available for the next main event. For the pharmaceutical industry, this means collecting hundreds of thousands of small-molecule compounds into libraries. Libraries are built to maximize the structural diversity of its members. The current paradigm is that screening these structurally diverse libraries for activity will select for a core structural component that is required for some specific activity. This core structure can be optimized by medicinal chemists by the addition and subtraction of substituents to improve its activity. This paradigm is a logical continuation of the concept of molecular interactions resembling the fit between a key and its lock. Molecular interactions are modeled by the component characteristics that allow one structure to bind to its partner. The current paradigm uses structure as the primary determinant for whether one molecule will bind to another.

A recent report in Proceedings of the National Academy of Science turns the current paradigm on its head (5). Led by a team at the Broad Institute in Cambridge, MA in collaboration with the Mathematical Institute of the Slovak Academy of Science, the report provides statistical support for the idea that better candidates are identified from libraries that maximize diversity of biological function over diversity of chemical structure. They argue that these libraries will increase the speed and decrease the cost of drug discovery by providing a greater number of hits in subsequent screens. This means that more robust candidates will brought into the main ring, creating more opportunities for the development of a successful drug.

To prove their point, the team collected tens of thousands of small-molecule compounds and categorized them by either structural diversity or performance diversity. Performance diversity was evaluated by changes in a biological cell following treatment with the compound. They monitored morphological features, such as cell size, shape, and changes to organelles, as well as gene expression patterns. The mechanism by which the compound affected cells was not important. Instead, they were looking for confirmation that the compound had any effect at all on a cell. After compiling two libraries, one that maximized structural diversity and one that maximized performance diversity, they tested each library in one hundred ongoing drug discovery projects. They found that the library that maximized performance diversity provided the greatest number of candidates for their discovery projects.

Small-molecule compounds are categorized by either structural diversity or performance diversity.

Performance diversity is evaluated by monitoring morphological feature changes in a biological cell following treatment: cell size, shape, changes to organelles, gene expression patterns. The mechanism by which the compound affects the cells matters less than the confirmation of its effectiveness.

After two libraries are compiled (respectively maximizing structural diversity and performance diversity), each library is tested in ongoing drug discovery projects. The library emphasizing performance diversity provided the greatest number of candidates for discovery projects.

Small-molecule compounds are categorized by either structural diversity or performance diversity.

Performance diversity is evaluated by monitoring morphological feature changes in a biological cell following treatment: cell size, shape, changes to organelles, gene expression patterns. The mechanism by which the compound affects the cells matters less than the confirmation of its effectiveness.

After two libraries are compiled (respectively maximizing structural diversity and performance diversity), each library is tested in ongoing drug discovery projects. The library emphasizing performance diversity provided the greatest number of candidates for discovery projects.

Small-molecule compounds are categorized by either structural diversity or performance diversity.

Performance diversity is evaluated by monitoring morphological feature changes in a biological cell following treatment: cell size, shape, changes to organelles, gene expression patterns. The mechanism by which the compound affects the cells matters less than the confirmation of its effectiveness.

After two libraries are compiled (respectively maximizing structural diversity and performance diversity), each library is tested in ongoing drug discovery projects. The library emphasizing performance diversity provided the greatest number of candidates for discovery projects.

Small-molecule compounds are categorized by either structural diversity or performance diversity.

Performance diversity is evaluated by monitoring morphological feature changes in a biological cell following treatment: cell size, shape, changes to organelles, gene expression patterns. The mechanism by which the compound affects the cells matters less than the confirmation of its effectiveness.

After two libraries are compiled (respectively maximizing structural diversity and performance diversity), each library is tested in ongoing drug discovery projects. The library emphasizing performance diversity provided the greatest number of candidates for discovery projects.

This report is exciting because it changes the way that compounds are evaluated for drug discovery. Structure is no longer the primary characteristic in determining whether a candidate should be considered for screening. By using biological function to assess candidacy, the first hurdle in drug discovery is designed to be a reflection of the final goal. In the end, a drug is successful because it accomplishes a specific function. Placing this requirement for biological activity at the beginning of the process guarantees that any candidate that makes it to the ring is able to put up a fight.

Juliesta Sylvester, Ph.D. is a biochemist who promotes innovation and technology transfer at the interface of academia and industry. Her research has spanned pharmaceutical discovery, molecular diagnostics, bioinformatics, and quantitative systems analysis. She is an avid world traveler, invited speaker at national and international meetings, and enthusiastic consultant for startups.

Juliesta Sylvester, Ph.D. is a biochemist who promotes innovation and technology transfer at the interface of academia and industry. Her research has spanned pharmaceutical discovery, molecular diagnostics, bioinformatics, and quantitative systems analysis. She is an avid world traveler, invited speaker at national and international meetings, and enthusiastic consultant for startups.

Juliesta Sylvester, Ph.D. is a biochemist who promotes innovation and technology transfer at the interface of academia and industry. Her research has spanned pharmaceutical discovery, molecular diagnostics, bioinformatics, and quantitative systems analysis. She is an avid world traveler, invited speaker at national and international meetings, and enthusiastic consultant for startups.

Juliesta Sylvester, Ph.D. is a biochemist who promotes innovation and technology transfer at the interface of academia and industry. Her research has spanned pharmaceutical discovery, molecular diagnostics, bioinformatics, and quantitative systems analysis. She is an avid world traveler, invited speaker at national and international meetings, and enthusiastic consultant for startups.